
import pandas as pd
from pandas import read_csv
import seaborn as sns

from sklearn.model_selection import TimeSeriesSplit
from sklearn.model_selection import train_test_split

import joblib as joblib

import matplotlib.pyplot as plt

import scikit_posthocs as scp
import scipy.stats as stats
import operator
import numpy as np
import statsmodels as st
import pingouin as pg

import math
import sklearn.metrics as sm

import plotly.express as px
from flask import Flask
import flask
from plotly.subplots import make_subplots
import plotly.graph_objects as go

C:\Users\Carmen\anaconda3\lib\site-packages\outdated\utils.py:14: OutdatedPackageWarning: The package outdated is out of date. Your version is 0.2.0, the latest is 0.2.1.
Set the environment variable OUTDATED_IGNORE=1 to disable these warnings.
  return warn(


RMSE_train = read_csv('../Datos_preprocesados/RMSE_errores_train.csv', encoding ='latin-1', sep = ',', na_values = ['NaN', 'NaT'])
RMSE_train.drop(columns = "Pliegues", inplace = True)
RMSE_train


RMSE_train.describe()


fig = px.box(RMSE_train, y = RMSE_train.columns, width = 700, height = 400)
fig.show()


print('Media \n',RMSE_train.mean(),'\n \n Desviacion tipica \n',RMSE_train.std())

Media 
 RF_lag3       0.049563
RF_lag5       0.051771
LR_lag3       0.032960
LR_lag5       0.032719
Lasso_lag3    0.029975
Lasso_lag5    0.030343
SVR_lag3      0.032815
SVR_lag5      0.033264
GBR_lag3      0.047135
GBR_lag5      0.050586
EN_lag3       0.029860
EN_lag5       0.030430
dtype: float64 
 
 Desviacion tipica 
 RF_lag3       0.048881
RF_lag5       0.051414
LR_lag3       0.027178
LR_lag5       0.027624
Lasso_lag3    0.028047
Lasso_lag5    0.028913
SVR_lag3      0.027536
SVR_lag5      0.027363
GBR_lag3      0.048149
GBR_lag5      0.052089
EN_lag3       0.028119
EN_lag5       0.028922
dtype: float64


MAE_train = read_csv('../Datos_preprocesados/MAE_errores_train.csv', encoding ='latin-1', sep = ',', na_values = ['NaN', 'NaT'])
MAE_train.drop(columns = "Pliegues", inplace = True)
MAE_train


MAE_train.describe()


fig = px.box(MAE_train, y = MAE_train.columns, width = 700, height = 400)
fig.show()


print('Media \n',MAE_train.mean(),'\n \n Desviacion tipica \n',MAE_train.std())

Media 
 RF_lag3       0.035397
RF_lag5       0.038609
LR_lag3       0.020654
LR_lag5       0.017455
Lasso_lag3    0.018104
Lasso_lag5    0.016971
SVR_lag3      0.022021
SVR_lag5      0.019194
GBR_lag3      0.033625
GBR_lag5      0.036978
EN_lag3       0.018395
EN_lag5       0.016443
dtype: float64 
 
 Desviacion tipica 
 RF_lag3       0.036337
RF_lag5       0.040109
LR_lag3       0.016466
LR_lag5       0.013860
Lasso_lag3    0.017191
Lasso_lag5    0.014463
SVR_lag3      0.017656
SVR_lag5      0.012644
GBR_lag3      0.037485
GBR_lag5      0.039825
EN_lag3       0.017260
EN_lag5       0.013971
dtype: float64


CC_train = read_csv('../Datos_preprocesados/CC_train.csv', encoding ='latin-1', sep = ',', na_values = ['NaN', 'NaT'])
CC_train.drop(columns = "Pliegues", inplace = True)
CC_train


CC_train.describe()


CC_train.mean()

RF_lag3       0.8485
RF_lag5       0.8744
LR_lag3       0.5515
LR_lag5       0.5783
Lasso_lag3    0.5620
Lasso_lag5    0.5881
SVR_lag3      0.5845
SVR_lag5      0.6130
GBR_lag3      0.7810
GBR_lag5      0.9318
EN_lag3       0.5693
EN_lag5       0.5943
dtype: float64


fig = px.box(CC_train, y = CC_train.columns, width = 700, height = 400)
fig.show()


print('Media \n',CC_train.mean(),'\n \n Desviacion tipica \n',CC_train.std())

Media 
 RF_lag3       0.8485
RF_lag5       0.8744
LR_lag3       0.5515
LR_lag5       0.5783
Lasso_lag3    0.5620
Lasso_lag5    0.5881
SVR_lag3      0.5845
SVR_lag5      0.6130
GBR_lag3      0.7810
GBR_lag5      0.9318
EN_lag3       0.5693
EN_lag5       0.5943
dtype: float64 
 
 Desviacion tipica 
 RF_lag3       0.095342
RF_lag5       0.094558
LR_lag3       0.255903
LR_lag5       0.274144
Lasso_lag3    0.235375
Lasso_lag5    0.252187
SVR_lag3      0.212598
SVR_lag5      0.257212
GBR_lag3      0.222787
GBR_lag5      0.079698
EN_lag3       0.225801
EN_lag5       0.236204
dtype: float64


df_predictor_lag3_escalado = read_csv('../Datos_preprocesados/predictor_lag3_escalado.csv', encoding='latin-1', sep = ',', na_values = ['NaN', 'NaT'])
df_predictor_lag3_escalado = df_predictor_lag3_escalado.set_index('Fecha')

df_predictor_lag5_escalado = read_csv('../Datos_preprocesados/predictor_lag5_escalado.csv', encoding='latin-1', sep = ',', na_values = ['NaN', 'NaT'])
df_predictor_lag5_escalado = df_predictor_lag5_escalado.set_index('Fecha')

X_train_3, X_test_3, y_train_3, y_test_3 = train_test_split(df_predictor_lag3_escalado.drop(['Incidentes'], axis=1),
                                        df_predictor_lag3_escalado['Incidentes'], train_size = 0.8, test_size = 0.2, 
                                        random_state = 42, shuffle = False)

X_train_5, X_test_5, y_train_5, y_test_5 = train_test_split(df_predictor_lag5_escalado.drop(['Incidentes'], axis=1),
                                        df_predictor_lag5_escalado['Incidentes'], train_size = 0.8, test_size = 0.2, 
                                        random_state = 42, shuffle = False)


modelos = []

modelos.append(joblib.load('rf_lag3_escalado.pkl'))
modelos.append(joblib.load('rf_lag5_escalado.pkl'))

modelos.append(joblib.load('regresion_lineal_lag3_escalado.pkl'))
modelos.append(joblib.load('regresion_lineal_lag5_escalado.pkl'))

modelos.append(joblib.load('lasso_lag3_escalado.pkl'))
modelos.append(joblib.load('lasso_lag5_escalado.pkl'))

modelos.append(joblib.load('svr_lag3_escalado.pkl'))
modelos.append(joblib.load('svr_lag5_escalado.pkl'))

modelos.append(joblib.load('gbr_lag3_escalado.pkl'))
modelos.append(joblib.load('gbr_lag5_escalado.pkl'))

modelos.append(joblib.load('en_lag3_escalado.pkl'))
modelos.append(joblib.load('en_lag5_escalado.pkl'))


nombres = ['RF Lag3', 'RF Lag5', 'LR Lag3', 'LR Lag5', 'Lasso Lag3', 'Lasso Lag5', 'SVR Lag3', 'SVR Lag5', 'GBR Lag3', 'GBR Lag5', 'EN Lag3', 'EN Lag5']


def pred_train_test(modelo, lag):
    
    if lag == 3:
        X_train = X_train_3
        y_train = y_train_3
        X_test = X_test_3
        y_test = y_test_3
    if lag == 5:
        X_train = X_train_5
        y_train = y_train_5
        X_test = X_test_5
        y_test = y_test_5
        
    fig = make_subplots(rows=1, cols=2)
    
    # Predicción en train
    y_pred_train_modelo = modelo.predict(X_train)
    # Predicción en test
    y_pred_test_modelo = modelo.predict(X_test)
    
    fig.add_trace(go.Scatter(x = y_train.index, y = y_pred_train_modelo, mode = 'lines', name = 'Train Real'), row = 1, col = 1)
    fig.add_trace(go.Scatter(x = y_train.index, y = y_train, mode = 'lines', name = 'Pred Train'), row = 1, col = 1)
    
    fig.add_trace(go.Scatter(x = y_test.index, y = y_pred_test_modelo, mode = 'lines', name = 'Test Real'), row = 1, col = 2)
    fig.add_trace(go.Scatter(x = y_test.index, y = y_test, mode = 'lines', name = 'Pred test'), row = 1, col = 2)
    
    fig.update_layout(showlegend = True, title_text = 'Predicciones Train VS Test ' + str(nombres[i]))
    
    flask.Markup(fig)
    
    fig.show("notebook")
    
    mae_train = sm.mean_absolute_error(y_train, y_pred_train_modelo)
    mae_test = sm.mean_absolute_error(y_test, y_pred_test_modelo)
    
    rmse_train = math.sqrt(sm.mean_squared_error(y_train, y_pred_train_modelo))
    rmse_test = math.sqrt(sm.mean_squared_error(y_test, y_pred_test_modelo))
    
    return mae_train, mae_test, rmse_train, rmse_test


maes_train = []
maes_test = []
rmses_train = []
rmses_test = []

for i in range(0, len(modelos)):
    nombre = nombres[i]
    # Si es numero par
    if i % 2 == 0:
        lag = 3
    else:
        lag = 5
    mae_train, mae_test, rmse_train, rmse_test = pred_train_test(modelos[i], lag)
    maes_train.append(mae_train)
    maes_test.append(mae_test)
    rmses_train.append(rmse_train)
    rmses_test.append(rmse_test)


maes_train = pd.Series(maes_train, index = nombres)
maes_test = pd.Series(maes_test, index = nombres)
rmses_train = pd.Series(rmses_train, index = nombres)
rmses_test = pd.Series(rmses_test, index = nombres)

RMSE_dif = round(rmses_test - rmses_train,2)
MAE_dif = round(maes_test - maes_train,2)

print('Diferencia MAE \n',MAE_dif)
print('Diferencia RMSE \n',RMSE_dif)

Diferencia MAE 
 RF Lag3       0.13
RF Lag5       0.13
LR Lag3       0.07
LR Lag5       0.06
Lasso Lag3    0.07
Lasso Lag5    0.06
SVR Lag3      0.07
SVR Lag5      0.06
GBR Lag3      0.12
GBR Lag5      0.13
EN Lag3       0.07
EN Lag5       0.06
dtype: float64
Diferencia RMSE 
 RF Lag3       0.20
RF Lag5       0.21
LR Lag3       0.10
LR Lag5       0.10
Lasso Lag3    0.10
Lasso Lag5    0.10
SVR Lag3      0.10
SVR Lag5      0.09
GBR Lag3      0.20
GBR Lag5      0.20
EN Lag3       0.10
EN Lag5       0.10
dtype: float64


RMSE_test = pd.read_csv('../Datos_preprocesados/RMSE_errores_test.csv')
RMSE_test.drop(columns = 'Predicciones', inplace = True)

MAE_test = pd.read_csv('../Datos_preprocesados/MAE_errores_test.csv')
MAE_test.drop(columns = "Predicciones", inplace = True)

CC_test = pd.read_csv('../Datos_preprocesados/CC_test.csv')
CC_test.drop(columns = "Predicciones", inplace = True)

columnas = CC_train.columns


RMSE_test


MAE_test


CC_test


def calcular_predicciones_lag3(modelo, y_pred, X_test):
    predicciones = []
    X_test_modelo = X_test.copy()
    for i in range(1,8):
        # Modificamos el conjunto de test
        X_test_modelo['Incidentes t-3'] = X_test_modelo['Incidentes t-2']
        X_test_modelo['Incidentes t-2'] = X_test_modelo['Incidentes t-1']
        X_test_modelo['Incidentes t-1'] = y_pred
        # Predecimos
        y_pred = modelo.predict(X_test_modelo)
        # Ajustamos al tamaño
        pred = y_pred[i:]
        predicciones.append(pred)
    return predicciones


def calcular_predicciones_lag5(modelo, y_pred, X_test):
    predicciones = []
    X_test_modelo = X_test.copy()
    for i in range(1,8):
        # Modificamos el conjunto de test
        X_test_modelo['Incidentes t-5'] = X_test_modelo['Incidentes t-4']
        X_test_modelo['Incidentes t-4'] = X_test_modelo['Incidentes t-3']
        X_test_modelo['Incidentes t-3'] = X_test_modelo['Incidentes t-2']
        X_test_modelo['Incidentes t-2'] = X_test_modelo['Incidentes t-1']
        X_test_modelo['Incidentes t-1'] = y_pred
        # Predecimos
        y_pred = modelo.predict(X_test_modelo)
        # Ajustamos al tamaño
        pred = y_pred[i:]
        predicciones.append(pred)
    return predicciones


def pred_test(modelo, lag, nombre):
    
    palette = plt.get_cmap('prism')
    
    if lag == 3:
        X_test = X_test_3
        y_test = y_test_3
    if lag == 5:
        X_test = X_test_5
        y_test = y_test_5
    
    # Predicción en train
    y_pred_modelo = modelo.predict(X_test)
    # Calculamos el error
    # Conjunto real = y_test
    
    if lag == 3:
        predicciones_modelo = calcular_predicciones_lag3(modelo, y_pred_modelo, X_test)
    if lag == 5:
        predicciones_modelo = calcular_predicciones_lag5(modelo, y_pred_modelo, X_test)
        
    plt.style.use('seaborn')
    
    plt.title('Real VS Test - '+nombre, loc = 'center', fontsize = 12, fontweight = 0, color = 'black')
    
    y_test.plot(linewidth = 1.2, alpha = 1, label = 'Incidentes Reales')
    pd.Series(y_pred_modelo).plot(linewidth = 1.2, alpha = 1, label = 'Incidentes t')
    
    for i in range(1,len(predicciones_modelo)+1):
        # Tomo la predicción en t+i
        s = pd.Series(predicciones_modelo[i-1])
        # Para mostrarla, la desplazo i valores a la derecha para colocarla en el dia correspondiente de prediccion
        s = s.shift(+i)
        s.dropna(axis = 0, inplace = True)
        s.plot(linewidth = 1.2, alpha = 1, label = 'Incidentes t+'+str(i))
        
    plt.legend(bbox_to_anchor=(1.3, 1.05))

    plt.xticks(rotation=90)
    plt.show()


nombres = ['LR Lag3', 'LR Lag5', 'Lasso Lag3', 'Lasso Lag5', 'SVR Lag3', 'SVR Lag5', 'EN Lag3', 'EN Lag5']


modelos = []

modelos.append(joblib.load('regresion_lineal_lag3_escalado.pkl'))
modelos.append(joblib.load('regresion_lineal_lag5_escalado.pkl'))

modelos.append(joblib.load('lasso_lag3_escalado.pkl'))
modelos.append(joblib.load('lasso_lag5_escalado.pkl'))

modelos.append(joblib.load('svr_lag3_escalado.pkl'))
modelos.append(joblib.load('svr_lag5_escalado.pkl'))

modelos.append(joblib.load('en_lag3_escalado.pkl'))
modelos.append(joblib.load('en_lag5_escalado.pkl'))


for i in range(0, len(modelos)):
    nombre = nombres[i]
    # Si es numero par
    if i % 2 == 0:
        lag = 3
    else:
        lag = 5
    pred_test(modelos[i],lag, nombre)


overfitting = []
for i in range(0, len(RMSE_dif)):
    if RMSE_dif[i] >= 0.2 or MAE_dif[i] >= 0.2:
        overfitting.append(i)
        
RMSE_test.drop(columns = RMSE_test.columns[overfitting], inplace=True)
RMSE_train.drop(columns = RMSE_train.columns[overfitting], inplace=True)
MAE_test.drop(columns = MAE_test.columns[overfitting], inplace=True)
MAE_train.drop(columns = MAE_train.columns[overfitting], inplace=True)
CC_test.drop(columns = CC_test.columns[overfitting], inplace=True)
CC_train.drop(columns = CC_train.columns[overfitting], inplace=True)


def pairwise_test(df, parametric = True, method = 'bonferroni', decreasing = True, ties=True):
    # Función que devuelve la matriz de rankings.
    # El dataframe de entrada df debe tener los modelos en las columnas y 
    # las medidas en las filas. 
    # parametric = indica si se aplica un test paramétrico (ttest_rel) o no paramétrico (wilcoxon)
    # decreasing = indica el sentido en el que es mejor la medida utilizada. 
    # ties = indca si se incluyen los empates en el resultado final 
    
    if decreasing:
        relate = operator.lt
    else:
        relate = operator.gt
    
    if parametric:
        test = stats.ttest_rel
    else:
        test = stats.wilcoxon
        
    cols = df.columns
    pvalues = np.array([])
    significant = np.array([])
    ranks = pd.DataFrame({"wins":np.zeros(len(cols)),
                         "ties":np.zeros(len(cols)),
                         "losses":np.zeros(len(cols))})
    ranks.index= cols
    for index1 in range(len(cols)):
        for index2 in range(index1+1,len(cols)):
            T,p = test(df.iloc[:,index1],df.iloc[:,index2])
            pvalues = np.append(pvalues,p)
            
    significant,padjust,a1,a2 = st.stats.multitest.multipletests(pvalues,alpha = 0.05,method = method)

    
    pos = 0;
    for index1 in range(len(cols)):
        for index2 in range(index1+1,len(cols)):
            if not significant[pos]:
                ranks.iloc[index1,1] = ranks.iloc[index1,1]+1
                ranks.iloc[index2,1] = ranks.iloc[index2,1]+1
            else:  
                if relate(df.iloc[:,index1].mean(),df.iloc[:,index2].mean()):
                    ranks.iloc[index1,0] = ranks.iloc[index1,0]+1
                    ranks.iloc[index2,2] = ranks.iloc[index2,2]+1
                else:
                    ranks.iloc[index2,0] = ranks.iloc[index2,0]+1
                    ranks.iloc[index1,2] = ranks.iloc[index1,2]+1
            pos = pos + 1
                
    ranks["diff"] = ranks.wins-ranks.losses
    #ranks = ranks.sort_values(by="diff",ascending = False)
    if  not ties:
        ranks.drop('ties',axis = 'columns', inplace = True)
    return ranks.astype(int)


RMSE_train.apply(stats.shapiro)


plt.style.use('seaborn-darkgrid')
f = plt.figure()
plt.title("Distribuiciones de los resultados RMSE")
RMSE_train.plot(kind = 'density', ax = f.gca(), alpha = 1, linewidth = 1.8)
f.gca().set_xlabel('RSME')
plt.legend(loc='right', bbox_to_anchor=(1.3,0.5))

<matplotlib.legend.Legend at 0x23a47904130>


test = stats.friedmanchisquare(*[RMSE_train[col] for col in RMSE_train.columns])
test

FriedmanchisquareResult(statistic=16.366666666666674, pvalue=0.021969618535374515)


scp.posthoc_nemenyi_friedman(RMSE_train, melted=False)


rsme_ranks = pairwise_test(RMSE_train, parametric=False, decreasing=True, ties= False, method="holm")
rsme_ranks


MAE_train.apply(stats.shapiro)


plt.style.use('seaborn-darkgrid')
f = plt.figure()
plt.title("Distribuiciones de los resultados MAE")
MAE_train.plot(kind = 'density', ax = f.gca(), alpha = 1, linewidth = 1.8)
f.gca().set_xlabel('MAE')
plt.legend(loc='right', bbox_to_anchor=(1.3,0.5))

<matplotlib.legend.Legend at 0x23a460eb580>


test = stats.friedmanchisquare(*[MAE_train[col] for col in MAE_train.columns])
test

FriedmanchisquareResult(statistic=18.23333333333335, pvalue=0.010960450248375383)


scp.posthoc_nemenyi_friedman(MAE_train, melted=False)


mae_ranks = pairwise_test(MAE_train, parametric=False, decreasing=True, ties= False, method="holm")
mae_ranks


CC_train.apply(stats.shapiro)


plt.style.use('seaborn-darkgrid')
f = plt.figure()
plt.title("Distribuiciones de los resultados CC")
CC_train.plot(kind = 'density', ax = f.gca(), alpha = 1, linewidth = 1.8)
f.gca().set_xlabel('CC')
plt.legend(loc='right', bbox_to_anchor=(1.3,0.5))

<matplotlib.legend.Legend at 0x23a4615daf0>


test = stats.friedmanchisquare(*[CC_train[col] for col in CC_train.columns])
test

FriedmanchisquareResult(statistic=12.65868263473052, pvalue=0.08087428551735754)


# Las tres tablas tienen las mismas columnas
modelos = CC_test.columns
tablas = []

for i in range(0, len(modelos)):
    df = pd.DataFrame()
    df['RMSE'] = RMSE_test[modelos[i]]
    df['MAE'] = MAE_test[modelos[i]]
    # En el caso de CC, cuanto más alto mejor, al contrario que RMSE y MAE
    df['CC'] = round(1 - CC_test[modelos[i]],3)
    # Guardamos en un csv
    df.to_csv(path_or_buf = '../Datos_preprocesados/multicriterio_'+modelos[i]+'.csv')
    # Añadimos a la lista
    tablas.append(df)


i = 0
for t in tablas:
    
    # Creamos df 
    datos = {'RMSE': t['RMSE'].values,
             'MAE': t['MAE'].values,
             'CC': t['CC'].values}

    df = pd.DataFrame(data = datos)

    fig = px.line(df, x = t.index, y = df.columns,
                  title = 'RMSE, MAE, CC para el modelo '+modelos[i], width = 700, height = 400)
    
    #t[['RMSE','MAE','CC']].plot(grid = True,title = modelos[i])
    
    flask.Markup(fig)
    
    fig.show()
    
    i = i+1


# Unimos todos los errores
union = pd.concat(tablas)
# Cogemos el minimo
minimo = union.min()
min_rmse = minimo[0]
min_mae = minimo[1]
min_cc = minimo[2]
# Cogemos el maximo
maximo = union.max()
max_rmse = maximo[0]
max_mae = maximo[1]
max_cc = maximo[2]


resultados = []

# Para cada modelo
for t in tablas:
    
    # Normalizamos el dataframe -> obtenemos los errores normalizados para el modelo
    t_norm = pd.DataFrame()
    t_norm['RMSE'] = (t['RMSE'] - min_rmse)/(max_rmse - min_rmse)
    t_norm['MAE'] = (t['MAE'] - min_mae)/(max_mae - min_mae)
    t_norm['CC'] = (t['CC'] - min_cc)/(max_cc - min_cc)

    # Ahora sumamos los errores -> obtenemos sRMSE, sMAE, sCC
    sRMSE = t_norm['RMSE'].sum()
    sMAE = t_norm['MAE'].sum()
    sCC = t_norm['CC'].sum()
    
    # Sumamos las diferencias absolutas error(i+1)-error(i)
    mas1_norm = t_norm.drop([0]).reset_index(drop = True)
    # Los valores obtenidos son mRMSE, mMAE, mCC
    diferencias_absolutas = abs(mas1_norm - t_norm).sum()
    
    # Sumamos el producto de los m y s
    v_modelo = sRMSE*(diferencias_absolutas['RMSE']) + sMAE*(diferencias_absolutas['MAE']) + sCC*(diferencias_absolutas['CC'])

    # Sumamos los tres s -> SIN USAR PENDIENTES
    #v_modelo = sRMSE + sMAE + sCC
    
    # Añadimos a la lista de resultados
    resultados.append(v_modelo)


for i in range(0,len(resultados)):
    print('Resultado modelo '+modelos[i]+' = '+str(resultados[i]))

Resultado modelo LR_lag3 = 9.973081944394607
Resultado modelo LR_lag5 = 11.331561457057548
Resultado modelo Lasso_lag3 = 8.385267771148376
Resultado modelo Lasso_lag5 = 6.0890359983325215
Resultado modelo SVR_lag3 = 9.274246203900406
Resultado modelo SVR_lag5 = 5.142233337678833
Resultado modelo EN_lag3 = 6.148534474568541
Resultado modelo EN_lag5 = 4.982633502976949


mejor_resultado = min(resultados)
mejor_modelo = resultados.index(mejor_resultado)
print('Mejor modelo = '+str(modelos[mejor_modelo]+' -> Resultado = '+str(mejor_resultado)))

Mejor modelo = EN_lag5 -> Resultado = 4.982633502976949

	RF_lag3	RF_lag5	LR_lag3	LR_lag5	Lasso_lag3	Lasso_lag5	SVR_lag3	SVR_lag5	GBR_lag3	GBR_lag5	EN_lag3	EN_lag5
0	0.010250	0.012253	0.040040	0.021895	0.010777	0.012175	0.015468	0.017608	0.009779	0.011421	0.009565	0.011302
1	0.003465	0.005035	0.006011	0.008859	0.005831	0.004163	0.006113	0.004916	0.003236	0.005702	0.005437	0.004665
2	0.002359	0.002950	0.002599	0.004666	0.002282	0.002253	0.007964	0.006088	0.002058	0.001663	0.002220	0.002200
3	0.005817	0.005213	0.008027	0.008158	0.006893	0.007117	0.012659	0.018517	0.005418	0.005386	0.006962	0.007347
4	0.019228	0.018612	0.015113	0.017661	0.014720	0.014294	0.018877	0.022049	0.018109	0.019953	0.015303	0.015526
5	0.097717	0.099564	0.030118	0.027917	0.031447	0.029454	0.031626	0.026445	0.094637	0.099538	0.031492	0.028937
6	0.054043	0.054168	0.047621	0.046455	0.048174	0.044944	0.050317	0.046399	0.049422	0.054665	0.048253	0.045141
7	0.117732	0.113723	0.064359	0.061369	0.063967	0.061773	0.066249	0.059950	0.118328	0.118405	0.064465	0.062454
8	0.124888	0.141807	0.085793	0.091884	0.085716	0.091056	0.089009	0.093796	0.121939	0.142605	0.085260	0.091053
9	0.060135	0.064386	0.029916	0.038327	0.029938	0.036199	0.029868	0.036866	0.048429	0.046525	0.029645	0.035670

	RF_lag3	RF_lag5	LR_lag3	LR_lag5	Lasso_lag3	Lasso_lag5	SVR_lag3	SVR_lag5	GBR_lag3	GBR_lag5	EN_lag3	EN_lag5
count	10.000000	10.000000	10.000000	10.000000	10.000000	10.000000	10.000000	10.000000	10.000000	10.000000	10.000000	10.000000
mean	0.049563	0.051771	0.032960	0.032719	0.029975	0.030343	0.032815	0.033264	0.047135	0.050586	0.029860	0.030430
std	0.048881	0.051414	0.027178	0.027624	0.028047	0.028913	0.027536	0.027363	0.048149	0.052089	0.028119	0.028922
min	0.002359	0.002950	0.002599	0.004666	0.002282	0.002253	0.006113	0.004916	0.002058	0.001663	0.002220	0.002200
25%	0.006925	0.006973	0.009799	0.011060	0.007864	0.008381	0.013362	0.017836	0.006509	0.007132	0.007613	0.008336
50%	0.036635	0.036390	0.030017	0.024906	0.022329	0.021874	0.024372	0.024247	0.033269	0.033239	0.022474	0.022231
75%	0.088322	0.090770	0.045726	0.044423	0.043993	0.042758	0.045644	0.044016	0.083333	0.088320	0.044063	0.042774
max	0.124888	0.141807	0.085793	0.091884	0.085716	0.091056	0.089009	0.093796	0.121939	0.142605	0.085260	0.091053

	RF_lag3	RF_lag5	LR_lag3	LR_lag5	Lasso_lag3	Lasso_lag5	SVR_lag3	SVR_lag5	GBR_lag3	GBR_lag5	EN_lag3	EN_lag5
0	0.008462	0.011392	0.032479	0.014992	0.007227	0.007082	0.012623	0.017494	0.007774	0.009879	0.006476	0.005981
1	0.003400	0.005021	0.004186	0.003734	0.004024	0.002734	0.005366	0.003984	0.003021	0.005115	0.003658	0.003443
2	0.001714	0.002173	0.001866	0.003266	0.001329	0.001685	0.005990	0.004277	0.001440	0.001243	0.001306	0.001632
3	0.002247	0.002934	0.003994	0.002763	0.002256	0.002159	0.007597	0.010134	0.001828	0.001652	0.001959	0.002001
4	0.012167	0.013229	0.011721	0.007150	0.011192	0.012083	0.014557	0.018390	0.012484	0.013561	0.012501	0.012333
5	0.084448	0.087158	0.016943	0.016096	0.017917	0.016959	0.021310	0.012787	0.082231	0.087985	0.020494	0.016918
6	0.033824	0.033456	0.026278	0.026127	0.025823	0.027261	0.034723	0.028557	0.022534	0.032382	0.026455	0.025546
7	0.065309	0.056947	0.036142	0.032730	0.035894	0.033170	0.040139	0.031767	0.074404	0.061051	0.037323	0.032217
8	0.098532	0.118379	0.052798	0.042889	0.055291	0.043395	0.059637	0.043899	0.099885	0.115925	0.054401	0.042336
9	0.043867	0.055397	0.020134	0.024806	0.020085	0.023185	0.018272	0.020647	0.030652	0.040988	0.019380	0.022019

	RF_lag3	RF_lag5	LR_lag3	LR_lag5	Lasso_lag3	Lasso_lag5	SVR_lag3	SVR_lag5	GBR_lag3	GBR_lag5	EN_lag3	EN_lag5
count	10.000000	10.000000	10.000000	10.000000	10.000000	10.000000	10.000000	10.000000	10.000000	10.000000	10.000000	10.000000
mean	0.035397	0.038609	0.020654	0.017455	0.018104	0.016971	0.022021	0.019194	0.033625	0.036978	0.018395	0.016443
std	0.036337	0.040109	0.016466	0.013860	0.017191	0.014463	0.017656	0.012644	0.037485	0.039825	0.017260	0.013971
min	0.001714	0.002173	0.001866	0.002763	0.001329	0.001685	0.005366	0.003984	0.001440	0.001243	0.001306	0.001632
25%	0.004666	0.006614	0.006070	0.004588	0.004825	0.003821	0.008853	0.010797	0.004210	0.006306	0.004363	0.004077
50%	0.022996	0.023343	0.018539	0.015544	0.014555	0.014521	0.016415	0.017942	0.017509	0.022971	0.015940	0.014626
75%	0.059948	0.056559	0.030929	0.025797	0.024388	0.026242	0.031370	0.026579	0.063466	0.056035	0.024965	0.024665
max	0.098532	0.118379	0.052798	0.042889	0.055291	0.043395	0.059637	0.043899	0.099885	0.115925	0.054401	0.042336

	RF_lag3	RF_lag5	LR_lag3	LR_lag5	Lasso_lag3	Lasso_lag5	SVR_lag3	SVR_lag5	GBR_lag3	GBR_lag5	EN_lag3	EN_lag5
0	0.660	0.851	0.396	0.465	0.431	0.433	0.485	0.465	0.592	0.875	0.458	0.450
1	0.725	0.647	0.065	-0.042	0.160	0.047	0.245	-0.009	0.269	0.734	0.202	0.109
2	0.866	0.907	0.467	0.556	0.486	0.571	0.491	0.553	0.646	0.895	0.497	0.571
3	0.926	0.934	0.337	0.433	0.320	0.445	0.336	0.535	0.871	0.942	0.315	0.442
4	0.863	0.889	0.568	0.601	0.561	0.602	0.689	0.748	0.786	0.981	0.571	0.598
5	0.928	0.932	0.793	0.803	0.796	0.803	0.824	0.826	0.938	0.985	0.796	0.802
6	0.837	0.910	0.517	0.583	0.508	0.600	0.477	0.690	0.909	0.976	0.501	0.598
7	0.943	0.944	0.888	0.913	0.887	0.910	0.860	0.873	0.966	0.981	0.884	0.907
8	0.933	0.948	0.864	0.885	0.862	0.879	0.817	0.811	0.989	0.981	0.861	0.879
9	0.804	0.782	0.620	0.586	0.609	0.591	0.621	0.638	0.844	0.968	0.608	0.587

Comparación de los modelos

Visualización de los errores¶

RMSE¶

Lectura de los datos y descripción estadística¶

MAE¶

Lectura de los datos y descripción estadística¶

CC¶

Lectura de los datos y descripción estadística¶

Gráficas Train VS Test + Errores¶

Errores test predicciones¶

Análisis estadístico¶

RMSE¶

MAE¶

CC¶

Algoritmo de Decisión Multicriterio¶

	RF_lag3	RF_lag5	LR_lag3	LR_lag5	Lasso_lag3	Lasso_lag5	SVR_lag3	SVR_lag5	GBR_lag3	GBR_lag5	EN_lag3	EN_lag5
0	0.22	0.23	0.13	0.13	0.13	0.13	0.13	0.13	0.21	0.21	0.13	0.13
1	0.23	0.23	0.18	0.16	0.16	0.14	0.16	0.14	0.22	0.21	0.16	0.14
2	0.23	0.23	0.21	0.17	0.20	0.16	0.19	0.15	0.22	0.22	0.19	0.15
3	0.23	0.23	0.23	0.19	0.21	0.18	0.20	0.17	0.23	0.22	0.20	0.17
4	0.23	0.23	0.27	0.25	0.23	0.22	0.23	0.21	0.23	0.22	0.22	0.21
5	0.23	0.24	0.30	0.25	0.26	0.23	0.26	0.21	0.23	0.22	0.24	0.22
6	0.24	0.24	0.32	0.30	0.27	0.25	0.27	0.24	0.23	0.22	0.25	0.24
7	0.24	0.24	0.35	0.33	0.28	0.28	0.30	0.27	0.23	0.22	0.26	0.27

	RF_lag3	RF_lag5	LR_lag3	LR_lag5	Lasso_lag3	Lasso_lag5	SVR_lag3	SVR_lag5	GBR_lag3	GBR_lag5	EN_lag3	EN_lag5
0	0.14	0.14	0.09	0.08	0.09	0.08	0.09	0.08	0.13	0.13	0.09	0.08
1	0.15	0.15	0.11	0.11	0.10	0.09	0.10	0.09	0.14	0.14	0.10	0.09
2	0.15	0.15	0.14	0.12	0.13	0.11	0.12	0.10	0.14	0.14	0.12	0.10
3	0.15	0.15	0.15	0.13	0.13	0.12	0.13	0.11	0.14	0.14	0.13	0.11
4	0.15	0.15	0.17	0.17	0.15	0.15	0.15	0.14	0.14	0.14	0.14	0.14
5	0.15	0.15	0.19	0.17	0.17	0.16	0.17	0.14	0.15	0.14	0.16	0.15
6	0.15	0.16	0.21	0.21	0.17	0.17	0.18	0.16	0.15	0.14	0.17	0.17
7	0.16	0.16	0.23	0.23	0.19	0.19	0.20	0.19	0.15	0.14	0.17	0.19

	RF_lag3	RF_lag5	LR_lag3	LR_lag5	Lasso_lag3	Lasso_lag5	SVR_lag3	SVR_lag5	GBR_lag3	GBR_lag5	EN_lag3	EN_lag5
0	0.847	0.859	0.903	0.907	0.903	0.907	0.904	0.905	0.852	0.860	0.904	0.908
1	0.841	0.861	0.841	0.876	0.865	0.894	0.868	0.896	0.832	0.861	0.872	0.901
2	0.838	0.862	0.838	0.870	0.835	0.881	0.842	0.890	0.830	0.859	0.846	0.888
3	0.837	0.861	0.837	0.850	0.835	0.862	0.839	0.875	0.824	0.861	0.844	0.869
4	0.836	0.861	0.836	0.803	0.807	0.829	0.810	0.840	0.824	0.857	0.822	0.840
5	0.835	0.859	0.835	0.813	0.790	0.834	0.788	0.845	0.822	0.855	0.807	0.840
6	0.833	0.857	0.833	0.774	0.785	0.817	0.778	0.823	0.819	0.850	0.802	0.827
7	0.831	0.855	0.831	0.762	0.767	0.799	0.757	0.801	0.815	0.850	0.788	0.809

	LR_lag3	LR_lag5	Lasso_lag3	Lasso_lag5	SVR_lag3	SVR_lag5	EN_lag3	EN_lag5
0	0.926488	0.894526	0.881280	0.884258	0.874022	0.886963	0.880959	0.884850
1	0.414248	0.190580	0.134983	0.145985	0.111330	0.156691	0.133844	0.148267

	LR_lag3	LR_lag5	Lasso_lag3	Lasso_lag5	SVR_lag3	SVR_lag5	EN_lag3	EN_lag5
LR_lag3	1.000000	0.900000	0.900000	0.751638	0.860792	0.900000	0.900000	0.860792
LR_lag5	0.900000	1.000000	0.642482	0.358005	0.900000	0.900000	0.587906	0.477509
Lasso_lag3	0.900000	0.642482	1.000000	0.900000	0.210653	0.860792	0.900000	0.900000
Lasso_lag5	0.751638	0.358005	0.900000	1.000000	0.068652	0.587906	0.900000	0.900000
SVR_lag3	0.860792	0.900000	0.210653	0.068652	1.000000	0.900000	0.172514	0.111334
SVR_lag5	0.900000	0.900000	0.860792	0.587906	0.900000	1.000000	0.806216	0.697062
EN_lag3	0.900000	0.587906	0.900000	0.900000	0.172514	0.806216	1.000000	0.900000
EN_lag5	0.860792	0.477509	0.900000	0.900000	0.111334	0.697062	0.900000	1.000000

	LR_lag3	LR_lag5	Lasso_lag3	Lasso_lag5	SVR_lag3	SVR_lag5	EN_lag3	EN_lag5
0	0.935417	0.912743	0.886039	0.917546	0.869829	0.946054	0.894715	0.919158
1	0.503236	0.300369	0.152955	0.336945	0.099507	0.622112	0.191507	0.349987

	LR_lag3	LR_lag5	Lasso_lag3	Lasso_lag5	SVR_lag3	SVR_lag5	EN_lag3	EN_lag5
0	0.958121	0.887266	0.957226	0.912596	0.934377	0.846537	0.948635	0.923018
1	0.764250	0.157936	0.753839	0.299302	0.492296	0.052824	0.652372	0.382816

	RF_lag3	RF_lag5	LR_lag3	LR_lag5	Lasso_lag3	Lasso_lag5	SVR_lag3	SVR_lag5	GBR_lag3	GBR_lag5	EN_lag3	EN_lag5
count	10.000000	10.000000	10.000000	10.000000	10.000000	10.000000	10.000000	10.000000	10.000000	10.000000	10.000000	10.000000
mean	0.848500	0.874400	0.551500	0.578300	0.562000	0.588100	0.584500	0.613000	0.781000	0.931800	0.569300	0.594300
std	0.095342	0.094558	0.255903	0.274144	0.235375	0.252187	0.212598	0.257212	0.222787	0.079698	0.225801	0.236204
min	0.660000	0.647000	0.065000	-0.042000	0.160000	0.047000	0.245000	-0.009000	0.269000	0.734000	0.202000	0.109000
25%	0.812250	0.860500	0.413750	0.487750	0.444750	0.476500	0.479000	0.539500	0.681000	0.906750	0.467750	0.480250
50%	0.864500	0.908500	0.542500	0.584500	0.534500	0.595500	0.556000	0.664000	0.857500	0.972000	0.536000	0.592500
75%	0.927500	0.933500	0.749750	0.752500	0.749250	0.752750	0.785000	0.795250	0.930750	0.981000	0.749000	0.751000
max	0.943000	0.948000	0.888000	0.913000	0.887000	0.910000	0.860000	0.873000	0.989000	0.985000	0.884000	0.907000